Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.
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ABSTRACT:
SUBMITTER: Fang LT
PROVIDER: S-EPMC8532138 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging new type of coronavirus that is responsible for the COVID-19 pandemic and the unprecedented global health emergency. Whole-genome sequencing (WGS) of SARS-CoV-2 plays a critical role in understanding the disease. Performance variation exists across SARS-CoV-2 viral WGS technologies, but there is currently no benchmarking study comparing different WGS sequencing protocols. We compared seven different SARS-CoV-2 WGS library protocols using RNA from patient nasopharyngeal swab samples under two storage conditions with low and high viral inputs. We found large differences in mappability and genome coverage, and variations in sensitivity, reproducibility, and precision of single-nucleotide variant calling across different protocols. For certain amplicon-based protocols, an appropriate primer trimming step is critical for accurate single-nucleotide variant calling. We ranked the performance of protocols based on six different metrics. Our findings offer guidance in choosing appropriate WGS protocols to characterize SARS-CoV-2 and its evolution.
| S-EPMC8294598 | biostudies-literature
Project description:Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.
| S-EPMC3383317 | biostudies-literature
Project description:Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X).We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies.Code is available on Github at: https://github.com/suyashss/variant_validation.suyashs@stanford.edu or mtaub@jhsph.edu.Supplementary data are available at Bioinformatics online.
| S-EPMC5408850 | biostudies-other
Project description:Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.0 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200119 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.0/v1.2.4 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.1.10 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.5.1 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.
| S-EPMC6966772 | biostudies-literature
Project description:MotivationOver the recent years, the field of whole-metagenome shotgun sequencing has witnessed significant growth owing to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster and with better coverage than before. This technical advancement has initiated the trend of sequencing multiple samples in different conditions or environments to explore the similarities and dissimilarities of the microbial communities. Examples include the human microbiome project and various studies of the human intestinal tract. With the availability of ever larger databases of such measurements, finding samples similar to a given query sample is becoming a central operation.ResultsIn this article, we develop a content-based exploration and retrieval method for whole-metagenome sequencing samples. We apply a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. We evaluate the performance of the proposed approach on two human gut metagenome datasets as well as human microbiome project metagenomic samples. We observe significant enrichment for diseased gut samples in results of queries with another diseased sample and high accuracy in discriminating between different body sites even though the method is unsupervised.Availability and implementationA software implementation of the DSM framework is available at https://github.com/HIITMetagenomics/dsm-framework.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4230234 | biostudies-literature
Project description:A good physical map is essential to guide sequence assembly in de novo whole genome sequencing, especially when sequences are produced by high-throughput sequencing such as next-generation-sequencing (NGS) technology. We here present a novel method, Feature sets-based Genome Mapping (FGM). With FGM, physical map and draft whole genome sequences can be generated, anchored and integrated using the same data set of NGS sequences, independent of restriction digestion. Method model was created and parameters were inspected by simulations using the Arabidopsis genome sequence. In the simulations, when ~4.8X genome BAC library including 4,096 clones was used to sequence the whole genome, ~90% of clones were successfully connected to physical contigs, and 91.58% of genome sequences were mapped and connected to chromosomes. This method was experimentally verified using the existing physical map and genome sequence of rice. Of 4,064 clones covering 115 Mb sequence selected from ~3 tiles of 3 chromosomes of a rice draft physical map, 3,364 clones were reconstructed into physical contigs and 98 Mb sequences were integrated into the 3 chromosomes. The physical map-integrated draft genome sequences can provide permanent frameworks for eventually obtaining high-quality reference sequences by targeted sequencing, gap filling and combining other sequences.
| S-EPMC5017645 | biostudies-literature
Project description:RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set.
| S-EPMC5431503 | biostudies-literature
Project description:Whole genome sequencing provides rapid insight into key information about the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), such as virus typing and key mutation site, and this information is important for precise prevention, control and tracing of coronavirus disease 2019 (COVID-19) outbreak in conjunction with the epidemiological information of the case. Nanopore sequencing is widely used around the world for its short sample-to-result time, simple experimental operation and long sequencing reads. However, because nanopore sequencing is a relatively new sequencing technology, many researchers still have doubts about its accuracy. The combination of the newly launched nanopore sequencing Q20+ kit (LSK112) and flow cell R10.4 is a qualitative improvement over the accuracy of the previous kits. In this study, we firstly used LSK112 kit with flow cell R10.4 to sequence the SARS-CoV-2 whole genome, and summarized the sequencing results of the combination of LSK112 kit and flow cell R10.4 for the 1200bp amplicons of SARS-CoV-2. We found that the proportion of sequences with an accuracy of more than 99% reached 30.1%, and the average sequence accuracy reached 98.34%, while the results of the original combination of LSK109 kit and flow cell R9.4.1 were 0.61% and 96.52%, respectively. The mutation site analysis showed that it was completely consistent with the final consensus sequence of next generation sequencing (NGS). The results showed that the combination of LSK112 kit and flow cell R10.4 allowed rapid whole-genome sequencing of SARS-CoV-2 without the need for verification of NGS.
| S-EPMC9612837 | biostudies-literature
Project description:BackgroundThe dog is frequently used as a model for hematologic human diseases. In this study the suitability of nine potential reference genes for quantitative RT-PCR studies in canine whole blood was investigated.FindingsThe expression of these genes was measured in whole blood samples of 263 individual dogs, representing 73 different breeds and a group of 40 mixed breed dogs, categorized into healthy dogs and dogs with internal and hematological diseases, and dogs that underwent a surgical procedure. GeNorm analysis revealed that a combination of 5 to 6 of the most stably expressed genes constituted a stable normalizing factor. Evaluation of the expression revealed different ranking of reference genes in Normfinder and GeNorm. The disease category and the white blood cell count significantly affected reference gene expression.ConclusionsThe discrepancy between the ranking of reference genes in this study by Normfinder and Genorm can be explained by differences between the experimental groups such as "disease category" and "WBC count". This stresses the importance of assessing the expression stability of potential reference genes for gene experiments in canine whole blood anew for each specific experimental condition.
| S-EPMC3045956 | biostudies-literature
Project description:Pharmacogenomics (PGx) investigates the influence of genetics on drug responses, enabling tailored treatments for personalized healthcare. This study assessed the accuracy of genotyping six genes using whole genome sequencing with four different computational tools and various sequencing depths. The effects of using different reference genomes (GRCh38 and GRCh37) and sequence aligners (BWA-MEM and Bowtie2) were also explored. The results showed generally minor variations in tool performance across most genes; however, more notable discrepancies were observed in the analysis of the complex CYP2D6 gene. Cyrius, a CYP2D6-specific tool, demonstrated the most robust performance, achieving the highest concordance rates for CYP2D6 in all instances, comparable to the consensus approach in most cases. There were rather small differences between the samples with 20× coverage depth and those with higher depth, but the decreased performance was more evident at lower depths, particularly at 5×. Additionally, variations in CYP2D6 results were observed when samples were aligned to different reference genomes using the same method, or to the same genome using different aligners, which led to reporting incorrect rare star alleles in several cases. These findings inform the selection of optimal PGx tools and methodologies as well as suggest that employing a consensus approach with two or more tools might be preferable for certain genes and tool combinations, especially at lower sequencing depths, to ensure accurate results. Additionally, we show how the upstream alignment can affect the performance of tools, an important factor to take into account.
| S-EPMC11315677 | biostudies-literature